Data for this session is available in Data – Perceptual Mapping

How to construct a map of product locations in the perceptual space of consumers How to do it using Minitab What attributes you should use when constructing a perceptual map
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What is Perceptual Mapping
A technique to understand the position of brands as consumers perceive them The output is a map of product locations in the perceptual space of consumers Though consumers may think about a number of attributes in evaluating products, it may be possible to summarize these attributes because consumer perceptions along these attributes may be correlated We can use factor analysis to find this reduced perceptual space and map the products in this space
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This allows us to select the # of factors PCA uses the correlation matrix of the data and constructs factors
if there are n variables we will have n factors first factor will explain most variance, second next and so on…

Second Step: Do Factor Analysis
Perform factor analysis with the factors selected from Step 1 Interpret resulting factors
use factor loadings and loading plot to interpret factors if it is not interpretable use rotation options until we get something that can be interpreted

Look at factor equations and factor scores
score plots will be useful
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We standardize the variables and then take the average of the 20 consumer’s ratings 19 on the standardized variable to plug in

Alternative Approach To Compute Factor Scores for Each Car

Store Minitab’s factor scores for each car based on each consumer’s ratings
we will have 20*6=120 numbers

Average the factor scores across consumers for each car
we will get the factor score for each car

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Where are Cars Located in the Perceptual Space?
Perceptual Map for Cars
1.5

Volkswagen

1 0.5

Neon

Camry
-1.5 Economy -1

Taurus
0 -0.5 -0.5 -1 -1.5 Fashion
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0

0.5

1

1.5

2

Lexus BMW

Applications of Perceptual Maps
Who are our competitors? On what dimensions do we compete? Where to introduce new products?
you also need to be aware of consumer preferences look for locations with relatively more consumers but limited competition

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Caveats
Identify relevant attributes
don’t miss important attributes (Exploratory Research is important) no point asking about unimportant attributes conjoint analysis may be useful in identifying what attributes are important to consumers

Identify discriminating attributes
don’t use primary attributes (like cleaning power of detergents) there should be real perceptual differences on average for the product
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Step 1: Choose Number of Factors to Extract
Do Principal Component Analysis (PCA) In Minitab select Stat>Multivariate>Principal Components… Select the variables you want to factor analyze in Variables box Select “Correlation” as the data that will be analyzed; this will mean that the data will be standardized and therefore each variable will have equal effect Ask for Scree Plot (using Graphs button) which graphs the amount of variance explained by each factor
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Step 2: Perform Factor Analysis
Do Factor Analysis in Minitab, Stat>Multivariate>Factor Analysis…. number of Factors to extract should be from Step 1 try “None” rotation for a start (else try Varimax or others if it doesn’t work) In Graphs: select loading plot (score plot is not useful here) In Storage: in the scores box store the factor scores by selecting 2 variables
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Step 3: Plot the Perceptual Map
Take the average of the factor scores for each car Use these average scores to plot the perceptual map